Description

Key Responsibilities:

• Design, develop, and deploy machine learning and deep learning models for predictive analytics, classification, regression, clustering, recommendation systems, and forecasting.
• Build end-to-end data science pipelines including data ingestion, feature engineering, model training, validation, and production deployment.
• Develop advanced analytics solutions using Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM).
• Perform exploratory data analysis (EDA) and statistical hypothesis testing using statistical techniques and probability modeling.
• Work extensively with SQL and RDBMS platforms to extract, transform, and analyze large datasets.
• Process and analyze large-scale datasets using big data technologies such as Spark, PySpark, or Hadoop ecosystems.
• Develop Natural Language Processing (NLP) models for text analytics, sentiment analysis, and language modeling where applicable.
• Implement model validation techniques, cross-validation, A/B testing, and performance evaluation metrics (ROC-AUC, precision-recall, RMSE, etc.).
• Deploy machine learning models into production using MLOps frameworks, CI/CD pipelines, Docker containers, and Kubernetes.
• Leverage cloud platforms (Azure, AWS, or GCP) for scalable model training, deployment, and monitoring.
• Develop and maintain automated model monitoring and drift detection frameworks.
• Create interactive dashboards and visualizations using Power BI, Tableau, or Matplotlib/Seaborn/Plotly to communicate insights to stakeholders.
• Collaborate with data engineers to optimize data pipelines and feature stores.
• Translate complex analytical results into actionable business insights for executive and non-technical stakeholders.
• Provide technical leadership, mentor junior data scientists, and contribute to enterprise AI strategy initiatives.

Required Skills & Qualifications:

• Advanced proficiency in Python for data science and machine learning development.
• Strong expertise in Machine Learning algorithms, supervised and unsupervised learning techniques.
• Experience with Deep Learning frameworks such as TensorFlow or PyTorch.
• Strong foundation in Statistics, Probability, Linear Algebra, and Predictive Modeling.
• Advanced SQL skills and experience working with enterprise RDBMS systems.
• Experience with Big Data platforms such as Spark or distributed computing frameworks.
• Hands-on experience deploying models using Docker, Kubernetes, and MLOps best practices.
• Experience working with cloud-based data science platforms (Azure ML, AWS SageMaker, or GCP AI Platform).
• Strong experience in data visualization and storytelling using BI tools.
• Experience working in Agile/Scrum environments and cross-functional teams

Key Skills

Education

Any Gradute